Using passive sonar for localizing multiple acoustic sources in shallow water is a fundamental task for underwater surveillance and monitoring systems. These systems typically use an array of hydrophones to collect sound generated by underwater acoustic sources without radiating sound to the water. Moreover, their impact on the maritime environment is minimal when compared to the one of similar systems using active sonar. Localization using passive sonar is complicated by the complexities of underwater acoustic propagation, interactions among multiple acoustic sources, and the uncertain dynamics of the acoustic propagation environment. Shipping lanes, marine life, and other environmental factors introduce noise that masks acoustic signals of interest and leads to low signal-to-noise ratios (SNRs) at the hydrophone array.
Matched-field processing (MFP) has been the workhorse for underwater source localization via passive sonar. MFP postulates a grid of tentative source locations and uses an acoustic propagation model to predict the acoustic pressure fields, also known as replicas, at the hydrophone array caused by an acoustic source at each grid location. The replicas are then “matched” to the acoustic measurements to generate the so-called ambiguity surface summarizing acoustic power estimates across the grid. Next, the localization problem is reduced to a peak-picking one over the ambiguity surface.
Despite its popularity, MFP has been challenged by scenarios with multiple sources and environmental mismatch. In narrowband passive-sonar-based localization, it has been empirically observed that using high frequencies yields high source resolution at the expense of increased sensitivity to model mismatch, whereas using low-frequencies yields reduced sensitivity to model mismatch at the expense of reduced source resolution. A need exists for an underwater source localization algorithm with improved resolution and resilience to model mismatch.